Lecture Notes  (Last updated 05 Mar 2009)

• Lecture 00 (Friday, 18 Jan 2008) - Machine Learning / Advanced Topics in AI
  Course Organization and Survey
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• Lecture 01 (Wednesday, 24 Jan 2008) - Introduction to Machine Learning andthe Version Space Formalism
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• Lecture 02 (Friday, 25 Jan 2008) - Representation Bias vs. Search Biasand Intro to Decision Trees
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• Lecture 04 (Friday, 30 Jan 2008) - Decision Trees
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• Lecture 05 (Friday, 01 Feb 2008) - Decision Trees, Occam¡¯s Razor, and Overfitting
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• Lecture 06 (Monday, 04 Feb 2008) - Bayes¡¯s Theorem, MAP,and Maximum Likelihood Hypotheses
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• Lecture 07 (Wed, 06 Feb 2008) - Simple (Na?ve) Bayesand Probabilistic Learning over Text
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• Lecture 08 (Frid, 08 Feb 2008) - Simple (Na?ve) Bayesand Probabilistic Learning over Text
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• Lecture 09 (Mon, 11 Feb 2008) - Perceptrons and Winnow
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• Lecture 11 (Mon, 15 Feb 2008) - Artificial Neural Networks: Backprop (continued) and Intro to SVM
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• Lecture 12 (Wed, 18 Feb 2008) - Support Vector Machines
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• Lecture 13 (Fri, 20 Feb 2008) - Support Vector Machines
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• Lecture 14 (Fri, 22 Feb 2008) - Instance-Based Learning (IBL): k-Nearest Neighbor and Radial Basis Functions
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• Lecture 15 (Mon, 25 Feb 2008) - Introduction to Genetic Algorithms
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• Lecture 16 (Wed, 27 Feb 2008) - Regression and Prediction
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• Lecture 17 (Fri, 29 Feb 2008) - Methods for Statistical Evaluation of Hypotheses
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• Lecture 18 (Mon, 03 Mar 2008) - Combining Classifiers: Weighted Majority, Bagging, and Stacking
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• Lecture 19 (Wed, 05 Mar 2008) - Combining Classifiers: Boosting the Margin and Mixtures of Experts
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• Lecture 20 (Fri, 07 Mar 2008) - Expectation Maximization (EM), Unsupervised Learning and Clustering
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• Lecture 21 (Mon, 10 Mar 2008) - Partitioning-Based Clustering and Expectation Maximization (EM)
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• Lecture 23 (Fri, 14 Mar 2008) - Density- and grid-based clustering
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• Lecture 24 (Mon, 24 Mar 2008) - Model-Based Clustering: Expectation-Maximization
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• Lecture 27 (Mon, 02 Apr 2008) - Time Series Data and Data Streams
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